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   "gpuType": "T4"
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  "accelerator": "GPU",
  "kaggle": {
   "accelerator": "nvidiaTeslaT4",
   "dataSources": [
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     "sourceId": 9068009,
     "sourceType": "datasetVersion",
     "datasetId": 5469316
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     "sourceType": "datasetVersion",
     "datasetId": 6444922
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   "source": [
    "import torch\n",
    "from collections import OrderedDict\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.io\n",
    "#from pyDOE import lhs#拉丁超立方抽样\n",
    "from torch import nn\n",
    "import time\n",
    "import warnings\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning)  # 只忽略UserWarning类型的警告"
   ],
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   "execution_count": 71
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   "cell_type": "code",
   "source": [
    "if torch.cuda.is_available():\n",
    "    device = torch.device('cuda')\n",
    "else:\n",
    "    device = torch.device('cpu')\n",
    "def setup_seed(seed):\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    np.random.seed(seed)\n",
    "    #  random.seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "setup_seed(124)\n",
    "dtype = torch.double"
   ],
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   "outputs": [],
   "execution_count": 72
  },
  {
   "id": "fe0818329fdb65c3",
   "cell_type": "code",
   "source": [
    "#求导\n",
    "def gradients(outputs, inputs):\n",
    "    return torch.autograd.grad(outputs, inputs, grad_outputs=torch.ones_like(outputs), create_graph=True)\n",
    "#类型转换\n",
    "def to_numpy(input):\n",
    "    if isinstance(input, torch.Tensor):\n",
    "        return input.detach().cpu().numpy()\n",
    "    elif isinstance(input, np.ndarray):\n",
    "        return input"
   ],
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  },
  {
   "id": "60a72b4d3732df3b",
   "cell_type": "code",
   "source": [
    "#构建网络layers\n",
    "def Dnnlayers(input_layers,output_layers,hidden_layers,neural):\n",
    "    layers = []\n",
    "    for i in range(hidden_layers + 2):\n",
    "        if i == 0:\n",
    "            layers.append(input_layers)\n",
    "        elif i == hidden_layers + 1:\n",
    "            layers.append(output_layers)\n",
    "        else:\n",
    "            layers.append(neural)\n",
    "    return layers"
   ],
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   "execution_count": 74
  },
  {
   "id": "4c739aded6291406",
   "cell_type": "code",
   "source": [
    "class DNN(nn.Module):#for u and f\n",
    "    def __init__(self, layers):\n",
    "        #继承父类\n",
    "        super(DNN, self).__init__()\n",
    "\n",
    "        #depth of network\n",
    "        self.depth = len(layers) - 1\n",
    "        #print(self.depth)\n",
    "\n",
    "        #activation of network\n",
    "        self.activation = nn.Tanh()\n",
    "        self.positive_activation = nn.Softplus()\n",
    "        #create the neural network\n",
    "        layers_list = list()\n",
    "        for i in range(self.depth - 1):\n",
    "            layers_list.append(\n",
    "                ('layer_%d' % i, nn.Linear(layers[i], layers[i+1]))#create each network\n",
    "            )\n",
    "            layers_list.append(\n",
    "                ('activation_%d' % i, self.activation)  #create each activation of network\n",
    "            )\n",
    "        layers_list.append(\n",
    "            ('layer_%d' % (self.depth - 1), nn.Linear(layers[-2], layers[-1]))\n",
    "            #last network do not have activation\n",
    "        )\n",
    "        #创建一个有序字典，其中包含了从 layers_list 中获得的键值对，这在需要保持元素顺序的场景（如神经网络层的顺序）中非常有用。\n",
    "        layerDict = OrderedDict(layers_list)\n",
    "\n",
    "        #deploy layers\n",
    "        self.layers = nn.Sequential(layerDict)\n",
    "        #print(self.layers)\n",
    "        self.positive_activation = nn.Softplus()  # 定义 Softplus 激活函数\n",
    "    #forword network,output the result of network\n",
    "    def forward(self, x):\n",
    "        #out = torch.exp(self.layers(x))\n",
    "        out = self.layers(x)\n",
    "        #out = self.positive_activation(out)  # 这里使用 Softplu#ln(e^x+1)\n",
    "        #out = self.positive_activation(out)\n",
    "        return out\n",
    "    \n",
    "class Conbined_Net(nn.Module):\n",
    "    def __init__(self, model1, model2):\n",
    "        super(Conbined_Net, self).__init__()\n",
    "        self.model1 = model1\n",
    "        self.model2 = model2\n",
    "    def forward(self, x):\n",
    "        g = self.model1(x)# model1 输出中间变量 g\n",
    "        #g_modified = torch.cat([g[:, 0:1], g[:, -1:]], dim=1)\n",
    "        # model2 接受 g_modified 作为输入并输出 f\n",
    "        f = self.model2(g)\n",
    "        return f"
   ],
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  },
  {
   "id": "d07f63b4c7b9d6b7",
   "cell_type": "code",
   "source": [
    "class Net_1_for_u():\n",
    "    def __init__(self, layers):\n",
    "        self.dnn = DNN(layers).double().to(device)\n",
    "class Net_2_for_f():\n",
    "    def __init__(self, layers):\n",
    "        self.dnn = DNN(layers).double().to(device)\n",
    "    def l2_loss(self):\n",
    "        l2_loss = torch.tensor(0.0, device=device, dtype=dtype, requires_grad=True)\n",
    "        for name,parma in self.dnn.named_parameters():\n",
    "            if 'bias' not in name:\n",
    "                l2_loss = l2_loss + (0.5 * torch.sum(torch.pow(parma,2)))\n",
    "        return l2_loss\n",
    "    def loss_pde(self, u, u_t):\n",
    "        f = self.dnn.forward(u)\n",
    "        f = ((u_t+f)**2).mean()\n",
    "        return f\n",
    "#\n",
    "class conbined_net():\n",
    "    def __init__(self, layers):\n",
    "        self.model1 = Net_1_for_u(layers)\n",
    "        self.Net1 = self.model1.dnn\n",
    "        self.model2 = Net2_model\n",
    "        self.Net2 = self.model2.dnn\n",
    "        self.Net = Conbined_Net(self.Net1, self.Net2)\n",
    "    def forward(self, x):\n",
    "        u = self.Net1.forward(x)\n",
    "        #rho, u, e = out1[:, 0:1], out1[:, 1:2], out1[:, 2:]#网络输出已改正，不需要重复exp\n",
    "        f = self.Net.forward(x)[:, 0:1]\n",
    "        return u, f\n",
    "    def loss_ic(self, x_ic, u_ic, f_ic):#有另一种写法\n",
    "        #for net1\n",
    "        u_ic_nn = self.Net1.forward(x_ic)[:, 0:1]\n",
    "        loss_ic1 = ((u_ic_nn- u_ic[:, 0:1]) ** 2).mean()\n",
    "        f_ic_nn = self.Net.forward(x_ic)\n",
    "        loss_ic2 = (((f_ic - f_ic_nn) ** 2).mean())\n",
    "\n",
    "        loss_ics = loss_ic1# + loss_ic2\n",
    "        return loss_ics\n",
    "    def loss_pde(self, x_int):\n",
    "        f = self.Net.forward(x_int)[:, 0:1]\n",
    "        u = self.Net1.forward(x_int)[:, 0:1]\n",
    "\n",
    "        #for net\n",
    "        du_g = gradients(u, x_int)[0]\n",
    "        u_t = du_g[:, :1]\n",
    "        \n",
    "        f = ((u_t + f)**2).mean()\n",
    "\n",
    "        return f"
   ],
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   "execution_count": 76
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   "cell_type": "code",
   "source": [
    "t_ic_train = np.linspace(0.0, 0.0, 1).reshape(-1, 1)\n",
    "u_ic = (6.0*np.exp(t_ic_train))/(9*np.exp(2.0*t_ic_train)+1)\n",
    "f_ic = u_ic*np.sqrt(1-u_ic**2)\n",
    "\n",
    "\n",
    "t_int_train = np.linspace(0.0, 2, 401).reshape(-1, 1)\n",
    "#\n",
    "t_ic_train = torch.tensor(t_ic_train, dtype=dtype, device=device, requires_grad=True)\n",
    "t_int_train = torch.tensor(t_int_train, dtype=dtype, device=device, requires_grad=True)\n",
    "u_ic = torch.tensor(u_ic, dtype=dtype, device=device)\n",
    "f_ic = torch.tensor(f_ic, dtype=dtype, device=device)"
   ],
   "id": "e335e1aa3a20a60d",
   "outputs": [],
   "execution_count": 77
  },
  {
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   },
   "cell_type": "code",
   "source": "Net2_model = torch.load('Net2_none_f_model.pth', map_location=device)",
   "id": "89196f92555158d2",
   "outputs": [],
   "execution_count": 78
  },
  {
   "id": "9f67a2ebd1efcaa8",
   "cell_type": "code",
   "source": [
    "#training\n",
    "def train(epoch):\n",
    "    model.Net1.train()\n",
    "    def closure():\n",
    "        optimizer.zero_grad()\n",
    "        for param in model.Net2.parameters():\n",
    "            param.requires_grad = False\n",
    "\n",
    "\n",
    "        loss_pde = model.loss_pde(t_int_train)\n",
    "        loss_ic = model.loss_ic(t_ic_train, u_ic, f_ic)\n",
    "        #total loss\n",
    "        loss =  loss_pde + 10*loss_ic\n",
    "\n",
    "        if epoch%10 ==0 :\n",
    "            print(f'epoch:{epoch}, loss:{loss:.8f},loss_pde:{loss_pde:.8f},loss_ic:{loss_ic:.8f}')\n",
    "\n",
    "\n",
    "\n",
    "        loss_total_history.append(to_numpy(loss))\n",
    "        loss_pde_history.append(to_numpy(loss_pde))\n",
    "\n",
    "        loss.backward()\n",
    "        return loss\n",
    "    loss = optimizer.step(closure)"
   ],
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  {
   "id": "24612c77b3401f6c",
   "cell_type": "code",
   "source": [
    "layers = Dnnlayers(1, 1, 5, 10)\n",
    "model = conbined_net(layers)"
   ],
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   "cell_type": "code",
   "source": [
    "loss_total_history = []\n",
    "loss_pde_history = []\n",
    "lr = 0.001\n",
    "alpha = 0.00001\n",
    "optimizer = torch.optim.Adam(model.Net1.parameters(), lr=lr)#,weight_decay=0.0001\n",
    "epochs1 = 15000\n",
    "tic = time.time()\n",
    "for epoch in range(1, epochs1+1):\n",
    "    train(epoch)\n",
    "toc = time.time()"
   ],
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    "colab": {
     "base_uri": "https://localhost:8080/"
    },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:10, loss:7.54985141,loss_pde:0.06084802,loss_ic:0.74890034\n",
      "epoch:20, loss:5.75787728,loss_pde:0.02925510,loss_ic:0.57286222\n",
      "epoch:30, loss:4.04499053,loss_pde:0.00622727,loss_ic:0.40387633\n",
      "epoch:40, loss:2.48710327,loss_pde:0.00206652,loss_ic:0.24850368\n",
      "epoch:50, loss:1.26489595,loss_pde:0.02663940,loss_ic:0.12382565\n",
      "epoch:60, loss:0.51424450,loss_pde:0.07082913,loss_ic:0.04434154\n",
      "epoch:70, loss:0.19760474,loss_pde:0.10716415,loss_ic:0.00904406\n",
      "epoch:80, loss:0.12280395,loss_pde:0.11812149,loss_ic:0.00046825\n",
      "epoch:90, loss:0.10894514,loss_pde:0.10816245,loss_ic:0.00007827\n",
      "epoch:100, loss:0.08908346,loss_pde:0.08805096,loss_ic:0.00010325\n",
      "epoch:110, loss:0.06682082,loss_pde:0.06682032,loss_ic:0.00000005\n",
      "epoch:120, loss:0.05145053,loss_pde:0.05073992,loss_ic:0.00007106\n",
      "epoch:130, loss:0.04308268,loss_pde:0.04180344,loss_ic:0.00012792\n",
      "epoch:140, loss:0.03908517,loss_pde:0.03815395,loss_ic:0.00009312\n",
      "epoch:150, loss:0.03712568,loss_pde:0.03668569,loss_ic:0.00004400\n",
      "epoch:160, loss:0.03565764,loss_pde:0.03544989,loss_ic:0.00002078\n",
      "epoch:170, loss:0.03414254,loss_pde:0.03398685,loss_ic:0.00001557\n",
      "epoch:180, loss:0.03259592,loss_pde:0.03242100,loss_ic:0.00001749\n",
      "epoch:190, loss:0.03107532,loss_pde:0.03087138,loss_ic:0.00002039\n",
      "epoch:200, loss:0.02957667,loss_pde:0.02936589,loss_ic:0.00002108\n",
      "epoch:210, loss:0.02809042,loss_pde:0.02789385,loss_ic:0.00001966\n",
      "epoch:220, loss:0.02661848,loss_pde:0.02644182,loss_ic:0.00001767\n",
      "epoch:230, loss:0.02516551,loss_pde:0.02500514,loss_ic:0.00001604\n",
      "epoch:240, loss:0.02373543,loss_pde:0.02358716,loss_ic:0.00001483\n",
      "epoch:250, loss:0.02233187,loss_pde:0.02219408,loss_ic:0.00001378\n",
      "epoch:260, loss:0.02095843,loss_pde:0.02083116,loss_ic:0.00001273\n",
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      "epoch:14960, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:14970, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:14980, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:14990, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:15000, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n"
     ]
    }
   ],
   "execution_count": 81
  },
  {
   "id": "86c0f9a889329a74",
   "cell_type": "code",
   "source": [
    "optimizer = torch.optim.LBFGS(model.Net1.parameters(),lr=1.0,max_iter=20)\n",
    "epochs2 = 2000\n",
    "tic = time.time()\n",
    "for epoch in range(1, epochs2+1):\n",
    "    train(epoch)\n",
    "toc = time.time()"
   ],
   "metadata": {
    "id": "86c0f9a889329a74",
    "trusted": true,
    "execution": {
     "iopub.status.busy": "2025-01-08T12:48:27.065211Z",
     "iopub.execute_input": "2025-01-08T12:48:27.065590Z",
     "iopub.status.idle": "2025-01-08T13:16:03.958133Z",
     "shell.execute_reply.started": "2025-01-08T12:48:27.065552Z",
     "shell.execute_reply": "2025-01-08T13:16:03.957165Z"
    },
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "outputId": "1d47db29-97f3-4ce6-f35c-0058712519a9",
    "ExecuteTime": {
     "end_time": "2025-02-28T14:29:20.973233Z",
     "start_time": "2025-02-28T14:29:16.069544Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "epoch:10, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:20, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
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      "epoch:1920, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
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      "epoch:1940, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:1950, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:1960, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:1970, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:1980, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:1990, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n",
      "epoch:2000, loss:0.00000016,loss_pde:0.00000016,loss_ic:0.00000000\n"
     ]
    }
   ],
   "execution_count": 82
  },
  {
   "id": "7cc2fa17c683c811",
   "cell_type": "code",
   "source": [
    "#预测\n",
    "t_test = np.linspace(0, 2, 201).reshape(-1, 1)\n",
    "u_exact = (6.0*np.exp(t_test))/(9*np.exp(2.0*t_test)+1)\n",
    "\n",
    "t_test_train = torch.tensor(t_test, dtype=dtype, device=device, requires_grad=True)\n",
    "u_predict = to_numpy(model.Net1.forward(t_test_train))[:, 0:1]\n",
    "#u\n",
    "plt.figure()\n",
    "plt.scatter(t_test, u_predict, label='u_Predict', marker='o', facecolor='none', edgecolor='red')\n",
    "plt.plot(t_test, u_exact, label='u_Exact', linewidth=3.0)\n",
    "plt.legend()\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('u')\n",
    "plt.savefig(r\"test_u_Predict_none_f.png\")\n",
    "plt.show()\n",
    "#u的error\n",
    "error_u= abs(u_predict-u_exact)\n",
    "plt.figure()\n",
    "plt.scatter(t_test, error_u, label='error_u', marker='o', facecolor='none', edgecolor='red')\n",
    "plt.legend()\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('error_u')\n",
    "plt.savefig(r\"test_error_u_none_f.png\")\n",
    "plt.show()\n",
    "#log\n",
    "plt.figure()\n",
    "plt.scatter(t_test, error_u, label='error_u', marker='o', facecolor='none', edgecolor='red')\n",
    "plt.legend()\n",
    "plt.yscale('log')\n",
    "plt.xlabel('t')\n",
    "plt.ylabel('error_u')\n",
    "plt.savefig(r\"test_error_u_none_f_log.png\")\n",
    "plt.show()\n",
    "#MSE\n",
    "print('MSE error for every var')\n",
    "print('error_u is ',(error_u**2).mean())\n",
    "print('*****************************************')\n",
    "#L2 error\n",
    "print('L2 error for every var')\n",
    "print('error_u is ',np.sqrt((error_u**2).mean()))\n",
    "print('*****************************************')\n",
    "#相对误差\n",
    "print('relative error for every var')\n",
    "print('error_u is ',np.sqrt(np.linalg.norm(error_u, ord=2)/np.linalg.norm(u_exact, ord=2)))\n"
   ],
   "metadata": {
    "id": "7cc2fa17c683c811",
    "execution": {
     "iopub.status.busy": "2025-01-08T13:29:30.317026Z",
     "iopub.execute_input": "2025-01-08T13:29:30.317353Z",
     "iopub.status.idle": "2025-01-08T13:29:39.269335Z",
     "shell.execute_reply.started": "2025-01-08T13:29:30.317329Z",
     "shell.execute_reply": "2025-01-08T13:29:39.267479Z"
    },
    "trusted": true,
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "outputId": "c1f4fb36-3b5f-4f05-9e99-43e89196227d",
    "ExecuteTime": {
     "end_time": "2025-02-28T14:29:21.574161Z",
     "start_time": "2025-02-28T14:29:20.974235Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MSE error for every var\n",
      "error_u is  3.5048724360323565e-07\n",
      "*****************************************\n",
      "L2 error for every var\n",
      "error_u is  0.0005920196310961619\n",
      "*****************************************\n",
      "relative error for every var\n",
      "error_u is  0.04344298411014883\n"
     ]
    }
   ],
   "execution_count": 83
  },
  {
   "id": "c226b9d91185272e",
   "cell_type": "code",
   "source": "torch.save(model, 'test_Net2_none_f_model.pth')",
   "metadata": {
    "id": "c226b9d91185272e",
    "ExecuteTime": {
     "end_time": "2025-02-28T14:29:21.588972Z",
     "start_time": "2025-02-28T14:29:21.576271Z"
    }
   },
   "outputs": [],
   "execution_count": 84
  },
  {
   "cell_type": "code",
   "source": [],
   "metadata": {
    "id": "wcGkpz3Nc93E",
    "ExecuteTime": {
     "end_time": "2025-02-28T14:29:21.604396Z",
     "start_time": "2025-02-28T14:29:21.590028Z"
    }
   },
   "id": "wcGkpz3Nc93E",
   "outputs": [],
   "execution_count": 84
  }
 ]
}
